colour_hdri.distortion.correct_vignette_RBF#

colour_hdri.distortion.correct_vignette_RBF(image: ArrayLike, characterisation_data: DataVignetteCharacterisation, smoothing: float = 0.001, kernel: Literal['linear', 'thin_plate_spline', 'cubic', 'quintic', 'multiquadric', 'inverse_multiquadric', 'inverse_quadratic', 'gaussian'] = 'cubic', epsilon: float = 1) → NDArrayFloat[source]#

Correct the vignette of given image using given characterisation for radial basis function (RBF) interpolation.

Parameters:

image (ArrayLike) – Image to correct the vignette of.
characterisation_data (DataVignetteCharacterisation) – Vignette characterisation data for given function.
smoothing (float) – Smoothing parameter, see scipy.interpolate.RBFInterpolator class.
kernel (Literal['linear', 'thin_plate_spline', 'cubic', 'quintic', 'multiquadric', 'inverse_multiquadric', 'inverse_quadratic', 'gaussian']) – Type of RBF, see scipy.interpolate.RBFInterpolator class.
epsilon (float) – Shape parameter that scales the input to the RBF, see scipy.interpolate.RBFInterpolator class.

Returns:

Vignette corrected image.

Return type:

numpy.ndarray

Examples

>>> image = apply_radial_gradient(np.ones([5, 7]))
>>> characterisation_data = characterise_vignette_RBF(image)
>>> np.around(correct_vignette_RBF(image, characterisation_data), 3)
array([[ 0.   ,  0.091,  0.841,  1.134,  0.841,  0.091,  0.   ],
       [ 0.   ,  0.967,  2.03 ,  2.552,  2.03 ,  0.967,  0.   ],
       [ 0.   ,  1.323,  2.647,  3.97 ,  2.647,  1.323,  0.   ],
       [ 0.   ,  0.967,  2.03 ,  2.552,  2.03 ,  0.967,  0.   ],
       [ 0.   ,  0.091,  0.841,  1.134,  0.841,  0.091,  0.   ]])